ABC inference from empirical data

At this point, you have a folder containing summary statistics and observed data to perform ABC inference. As explained in our home page, we performed the ABC inference using ABCreg. However, you can used other ABC software to perform the inference.

We link ABCreg with Julia to perform ABC inference. If you are going to use ABCreg to make inferences from our software directly, please cite the publication. We used our Julia interface to ABCreg o run the inference. We set the tolerance value such that 2500 acceptances were recorded for the inference

posteriors = MKtest.ABCreg(analysis_folder="mktest/",S=size(adap.dac,1),tol=0.025,abcreg="/home/jmurga/ABCreg/src/reg");

The function will output one file per dataset containing the posteriors distributions. The posterior distributions contains five columns corresponding to:

• α weak: Contribution of weak selecction to $\alpha$
• α strong: Contribution of strong selecction to $\alpha$
• α: Adaptation rate
• γ: Negative selection coefficient
• β: DFE shape parameter

You can check multiple statistics from posteriors distribution using MKtest.summary_abc function. Please check MKtest.summary_abc, you can approximate the inference using different statistics such as the mode, the mean or the median from posterior.

df_summary, parameter_inference = MKtest.summary_abc(posteriors)